We evaluate the representation capabilities of the proposed approach for graph reconstruction. Following previous work [1]}, [2]}, we measure the quality of the learned embeddings by reporting average distortion \(D_{avg}\)  (global metric, lower is better) and mean average precision (mAP, local metric, higher is better) as fidelity measures (formulas in Appendix REF ).
